Category: Technical How-to

Introduction Continuous improvement depends on experimentation. Teams know that the fastest path to better outcomes is to test changes against real user behavior, measure results, and iterate. In practice, sustaining that cycle is slow and costly because the overhead compounds with each attempt. Three barriers slow teams down: 1. Planning cost — Turning a proposed […]

AWS Health Planned Lifecycle Events signal when a managed service version is nearing end of standard support. Learn how to automate these upgrades end to end with AWS DevOps Agent and Kiro: detect the event, investigate the upgrade path, apply validated code changes, and open a pull request for human review.

Introduction Running workloads on Amazon Elastic Kubernetes Service (Amazon EKS) can involve managing failures like OOMKilled or IP exhaustion. Engineers must repeatedly collect pod logs, trace events, and check node logs—a process that slows at night/weekends, with critical data lost when pods are deleted or nodes become unhealthy. This collection phase is pure overhead on […]

Introduction Development velocity has reached new heights with AI-driven development tools and practices. Organizations are generating code faster than ever before. But that speed carries risk. Researchers Anderson, Parker, and Tan warned in MIT Sloan Management Review, “Legacy systems tend to carry hidden debt; layering AI-generated code on top of them creates additional tangled dependencies.” […]

This post was co-written with Michael Stephan, Senior Principal Product Manager, and Christian Kreuzberger, Principal Software Engineer, at Dynatrace. AI-driven software delivery changes how code gets written, but not what production demands of it. A generated change still has to fit the traffic your service receives, the dependencies it calls, and the capacity limits it […]

Introduction When CI/CD deployment failures occur for GitHub hosted applications,  AWS DevOps Agent reduces the hours that Development and Site Reliability Engineering (SRE) teams typically spend manually investigating across multiple AWS services, logs, and pipeline stages. This process delays critical deployments and impacts software delivery velocity. This is especially true when teams need to correlate data between GitHub commit histories, AWS CodePipeline execution logs, and Amazon […]

Introduction Large-scale machine learning workloads: training, fine-tuning, and inference run on clusters of hundreds to thousands of GPU instances for days or weeks at a stretch. Keeping operational visibility across a fleet of this size is a constant challenge: hardware health events, node lifecycle transitions, capacity fluctuations, and workload-level issues appear in the event stream around […]

“A pull request comes back with a single comment: “This doesn’t follow our circuit breaker pattern. Check the Architectural Decision Record .”  You know the architecture decision record exists somewhere. You open your team’s wiki, search “circuit breaker,” scroll past six irrelevant results, find the document, read through it, switch back to your editor, and fix the […]

This post is co-written with Govind Menon, Head of MCP Product at ServiceNow. Introduction Enterprise teams managing applications on AWS often rely on ServiceNow as their IT service management (ITSM) system for incident tracking, change management, and configuration management. When incidents occur, engineers must context-switch between AWS, third party observability tools and ServiceNow, manually correlating […]

Walk through a complete CloudFormation development cycle – authoring, validation, deployment, and troubleshooting – without leaving your AI assistant, using the AWS IaC MCP Server.